In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.

This letter presents a novel locally adaptive error concealment method for subband coded images. For each lost low-frequency coefficient, we estimate the optimal interpolation weights from its neighborhood. The calculation of the interpolation weights is optimized in the mean squared error sense, and it takes into account the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to methods of similar complexity, the proposed scheme estimates the lost coefficients more accurately: on average, the PSNR is increased by up to 4.5 dB. The reconstructed images also look better, and our method is fast and of low complexity.

In image communication over lossy packet networks (e.g., cell phone communication), packet loss errors lead to damaged images. Damaged images can be repaired with passive error concealment methods, which use neighboring coefficient or pixel values to estimate the missing ones. Neighboring image data should, thus, be spread over different packets. This paper presents a novel robust packetization method for the transmission of image content in lossy packet networks. We first define novel criteria for a good packetization. Based on these properties, we propose a cost function for packetization masks. We then use stochastic optimization to calculate optimal packetization masks. We test our packetization technique on both wavelet coding and DCT coding. Compared to other packetization techniques, we are able to achieve the same or better mean quality of the reconstructed images but with less fluctuation in quality, which is important for the viewer experience. In this way, we significantly increase the worst case quality, especially for high packet loss rates. This leads to visually more pleasing images in case of a passive reconstruction.

This paper presents a novel passive error concealment method for wavelet coded images. The proposed method is a locally adaptive directional interpolation approach, where the interpolation weights are estimated based on the available local context. For each lost low frequency coefficient, we estimate the optimal interpolation weights based on the errors that would arise by horizontally and vertically interpolating the available neighbors of the lost coefficient. Compared to older methods of similar complexity, the proposed scheme estimates the lost coefficients much better: on average, the PSNR is increased with up to 0.6 dB. The results also indicate improvements over the best available state-of-the-art techniques. The reconstructed images also look better. As our method is fast and of low complexity, it is widely usable.

In packet switched networks such as the Internet, packets may get lost during transmission due to, e.g., network congestion. This leads to a quality degradation of the original signal. As video communication is a bandwidth consuming application, the original data are first compressed. This compression step increases the impact of information loss even more. In wavelet based image and video coding, the low frequency data is the most important. Loss of low frequency coefficients results in annoying black holes in the received images and video. This effect can be countered by post processing error concealment: a lost coefficient is estimated from its neighboring coefficients. In this paper we present a locally adaptive interpolation method for the lost low frequency coefficients. For each lost low frequency coefficient, we estimate the optimal interpolation direction (horizontal or vertical) using novel error measures. In this way, we preserve the edges in the reconstructed image much better. Compared to older techniques of similar complexity, our scheme reconstructs images with the same or better quality. This is reflected in the visual as well as in the numerical results: there is an increase of up to 4.4 dB compared to bilinear concealment. The proposed scheme is fast and simple, which makes it suitable for real-time applications.